# Accession Number:

## AD0698468

# Title:

## A NEW ESTIMATION THEORY FOR SAMPLE SURVEYS.

# Descriptive Note:

## Technical rept.,

# Corporate Author:

## TEXAS A AND M UNIV COLLEGE STATION

# Personal Author(s):

# Report Date:

## 1968-01-01

# Pagination or Media Count:

## 22.0

# Abstract:

A new estimation theory for sample surveys is proposed. The basic feature of the theory is a special parametrization of finite populations based on the assumption that a character attached to the units is measured on a known scale with a finite set of scale points. In the class of estimators which do not functionally depend on the identification labels preattached to the units, the following results are proved 1 For simple or stratified simple random sampling without replacement, the customary estimators are unbiased minimum variance. 2 For simple random sampling with replacement, the sample mean based only on the distinct units in the sample is the maximum likelihood estimator of the population mean. 3 If a concomitant variable with known population mean is also observed, an approximation to the maximum likelihood estimator of the population mean is closely related to the customary regression estimator. 4 If prior information in the form a prior distribution is available, Bayes estimators can be derived using the complete likelihood. Author

# Descriptors:

# Subject Categories:

- Statistics and Probability